• 한국운동역학회지
• /
• 제14권3호
• /
• pp.49-65
• /
• 2004

The purpose of this study is development of posture evaluation and Range of Motion(ROM) system by using digital vision analysis method. The results of this study are as follows. First, Scoliosis evaluation through this research measurement system represent 3mm error in 7 cervical point and deepest lumbar point, 0.7mm error in other point. This mean this research measurement system have a reliability for scoliosis evaluation. Second, for spine line evaluation on high fat subject, we need reconstrection spine line after measurement for fat thickness in 7 cervical point and deepest lumbar point. Third, In pedioscope error test, it present 0.01848cm in X axis and 0.01757cm in Y axis. This results mean pedioscope have a reliability foot evaluation. Forth, Posture evaluation and Range of Motion measurement system by using digital vision analysis method can fast measure in range of motion and foot evaluation and posture. therefore we can expect this system application in young people posture clinic center and hospital and so on.

• 한국신경인지재활치료학회지
• /
• 제10권2호
• /
• pp.19-26
• /
• 2018

목적: 본 거북목 교정용 의자는 사람의 엉덩이와 정강이를 지지하여 사람의 자세를 교정하는 의자로서, 정강이를 경사상으로 지지하는 정강이 지지대와 정강이가 정강이 지지대에 경사 상으로 지지되었을 때 사람의 엉덩이를 경사 상으로 지지하는 엉덩이 지지대와 정강이 지지대와 엉덩이 지지대가 각각 결합되어 지지되는 메인프레임과 엉덩이 지지대 상부에 결합되며 엉덩이의 형상에 상응하여 유동적으로 함입되는 유동시트를 포함하며, 유동시트에 엉덩이가 균등하게 함입되도록 함으로써 불안정한 엉덩이 지지면을 제공하여, 지속적으로 자세에 대한 자극을 주어 사람이 자세를 인지함에 따라 교정되도록 고안된 자세 교정용 의자이다. 결과: 자세 교정용 의자에 사람이 착석하게 되면, 정강이와 엉덩이가 경사 상으로 지지되고 이에 따라 허리가 펴지게 되는데, 허리의 펴짐에 따라 어깨가 뒤로 젖혀지면서 가슴이 펴지고 목이 몸의 중앙에 위치하게 되어 사람의 자세를 교정할 수 있다. 불균형한 자세는 의자에 착석한 사람에게 자세 불량에 따른 불편함을 초래하고, 불편함을 해소하기 위해 자세 교정용 의자에 착석한 사람은 엉덩이의 균형을 잡기 위해 지속적이고 반복적으로 자세를 교정하여 신체의 밸런스를 유지하게 된다. 이러한 과정에서 사람의 좌우 방향의 자세를 교정함으로써 궁극적으로 자세 교정의 효과를 높일 수 있게 된다. 결론: 향후, 본 교정용 의자를 사용한 거북목 자세를 가진 사람에 있어서 지속적인 자세교정에 대한 집단연구가 필요하다.

• 전기학회논문지
• /
• 제65권9호
• /
• pp.1557-1563
• /
• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 추계종합학술대회 논문집
• /
• pp.483-486
• /
• 1999

In this paper, we present a real-time hand gesture recognition system that controls motion of a human avatar based on the pre-defined dynamic hand gesture commands in a virtual environment. Each motion of a human avatar consists of some elementary motions which are produced by solving inverse kinematics to target posture and interpolating joint angles for human-like motions. To overcome processing time of the recognition system for teaming, we use a Fuzzy Min-Max Neural Network (FMMNN) for classification of hand postures

• 경영과정보연구
• /
• 제4권
• /
• pp.395-407
• /
• 2000

It is necessary to develop method that human and computer can interfact by the hand gesture without any special device. In this thesis, the real time hand gesture recognition was developed. The system segments the region of a hand recognizes the hand posture and track the movement of the hand, using computer vision. And it does not use the blue screen as a background, the data glove and special markers for the recognition of the hand gesture.

• 전자공학회논문지B
• /
• 제28B권7호
• /
• pp.578-589
• /
• 1991

In this paper, there is proposed a novel concept of Incremintal Circle Transform which can describe the boundary contour of a two-dimensional object without object without occlusions. And a pattern recognition algorithm to determine the posture of an object is developed with the aid of line integral and similarity transform. Also, It is confirmed via experiments that the algorithm can find the posture of an object in a very fast manner independent of the starting point for boundary coding and the position of the object.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2013년도 춘계학술대회 논문집
• /
• pp.1349-1350
• /
• 2013

• 제어로봇시스템학회논문지
• /
• 제21권12호
• /
• pp.1100-1108
• /
• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.877-885
• /
• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• 정보과학회 컴퓨팅의 실제 논문지
• /
• 제21권8호
• /
• pp.549-554
• /
• 2015

최근 자연스러운 인터페이스 하드웨어의 증가와 더불어 사용자의 자세를 인식하는 콘텐츠 산업이 부상하고 있다. 컴퓨터 비전 기술은 고가 하드웨어 장치의 대안으로 콘텐츠 산업의 발전에 효율적이다. 본 논문에서는 생방송으로 진행되는 시청자 참여형 양방향 TV 프로그램에 적합한 시청자의 포스처를 인식하는 방법을 제안한다. 제안하는 방법은 검출된 얼굴 위치에서 획득한 사용자 얼굴색과 모션맵을 사용하여 사용자의 손 위치를 안정적으로 검출하고, 위치 관계 분석을 통해 포스처를 인식한다. 제안하는 방법은 복잡한 배경에서도 생방송 양방향 TV 프로그램에서 사용되는 세 가지 자세를 인식하는데 90%의 인식 성능이 나타나는 것을 실험을 통해 확인하였다.